Mobile source emissions from roadways near human populations often result in greater health impacts than emissions from industrial facilities in North America [1,2], primarily due to their proximity to densely populated areas and the high exposure to pollutants such as mobile source air toxics (MSATs).
Mobile source emissions from roadways near human populations often result in greater health impacts than emissions from industrial facilities in North America [1,2], primarily due to their proximity to densely populated areas and the high exposure to pollutants such as mobile source air toxics (MSATs).
There is a problem with the traditional use of worst-case or average exposure values in human health risk assessments for MSATs [2], which results in overlooking the considerable variability among individuals in a population, the specific vulnerabilities of sensitive subpopulations, and the inability to capture episodic high-exposure events that considerably impact risk assessment outcomes.
Stochastic methods address these challenges by capturing variability and uncertainty for a more comprehensive risk assessment. Deterministic methods provide baseline exposure–response relationships, while stochastic methods account for the range of possible exposures in different individuals, reflecting real-world complexities. A new stochastic method for MSATs is proposed to provide a more accurate and comprehensive assessment of health risks, paving the way for more effective and equitable risk management strategies.
Article by Mohammad Munshed
Photo Credit: aled7 via Pixabay
