Chemicals present in automobile traffic tunnels and the possible community health hazards: A review of the literature
Bibliographic record
Abstract
Dozens of volatile and semivolatile organic compounds can be detected in vehicle exhaust, along with numerous metals and oxides of sulfur, nitrogen, and carbon. While the adverse effects of these chemicals have been extensively studied surrounding open roadways, the hazards to local residents and commuters resulting from the presence of tunnel emission chemicals are less well known. Commuters and workers within tunnels are also exposed to tunnel atmospheres, and the risks have only been evaluated to a limited extent. Approximately 50 studies conducted at more than 35 different international traffic tunnels were reviewed in order to characterize the potential health impact on individuals residing near these tunnels. One objective of this article is to identify those chemicals that deserve further study in order to understand the hazards to humans who work in these tunnels, as well as the risks to those in the surrounding community. The second objective is to present the available information regarding the hazards to those living near these tunnels. The published information, for the most part, indicates that the concentration of most toxicants detected in communities exposed to tunnel emissions are below those concentrations that are generally considered to pose either a significant acute or chronic health hazard. However, there have been no comprehensive studies that have evaluated the concentration of all of the relevant toxicants on a real-time basis or using repetitive time-weighted average sampling. Based on our analysis of the existing information appearing in peer-reviewed literature and government reports, additional information on the variation of concentrations of various chemicals over time near the tunnel exits would be helpful. Optimally, these would be better if evaluated in conjunction with traffic magnitude and vehicle type. It would also be useful to further characterize acute exposures to commuters or tunnel workers during times of heavy volume or slow-moving traffic due to accidents within the tunnel structure, when tunnel pollutant levels would be expected to be substantially elevated. A recent review by the Australia's National Health and Medical Research Council also discusses tunnel and air quality in detail (2008). Nearly 300 references are cited.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".