Methodology for Mobile Toxics Deterministic Human Health Risk Assessment and Case Study
Bibliographic record
Abstract
Air toxic emissions from on-road mobile sources are significant contributors to the degradation of air quality in urban and dense population centers. Research led by the United States Environmental Protection Agency (EPA) identified more than 1162 hazardous air pollutants (HAPs) in the exhaust and evaporative emissions from on-road mobile sources. However, less than 70 hazardous air pollutants are monitored by regulatory agencies. HAPs emitted from Mobile Sources are known as Mobile Source Air Toxics (MSATs). The EPA estimates that approximately half of the cancer risk and 74% of noncancer health impacts from air toxics is attributed to mobile sources. The quantification of the risk associated with MSATs exposure remains limited to date, and only a few MSATs have ambient air quality standards to protect human health and welfare. This work presents a novel and validated methodology to quantify the myriad health risks associated with exposure to on-road mobile emissions. This methodology is introduced in the form of a pipelined analysis process, which may be employed in existing and new transportation projects. The proposed new methodology integrates results from three different types of models: on-road vehicle emissions inventory models such as MOVES and IVE, air dispersion models such as AERMOD and SCIPUFF, and risk estimate models for human and ecological receptors such as the 2005 Final U.S. EPA Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities. The result of this research work is a new methodology that provides regulators and risk analysts with a more detailed awareness of the health impacts of MSATs. A case study of Saint Paul, Minnesota, validated the air dispersion modeled results against monitored data, and the agreement was acceptable (i.e., the estimates were within a factor of two of the observations). Three high-population locations in the Saint Paul area were evaluated for human health risk, with the observation that at two of these locations, the Saint Paul—Ramsey Health Center and Anderson Office Building, the calculated cancer risk is in excess of the target risk level of 1.0E-05 for benzo(a)pyrene. The methodology presented in this paper allows regulators, risk analysts, and air quality engineers to better estimate multi-pathway cancer and noncancer risk associated with acute and chronic exposure to MSATs. Moreover, this work provides a science-based aid to policy decision makers when considering factors that most significantly affect population health and ecology.
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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 | 0.002 | 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 | 0.001 | 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".