Exploring Regional Reduction Pathways for Human Exposure to Fine Particulate Matter (PM_2.5) Using a Traffic...

Area of Science:

• Environmental Science
• Transportation Engineering
• Public Health

Background:

• Fine particulate matter (PM2.5) from vehicle emissions poses significant health risks.
• Accurate quantification of PM2.5 exposure is crucial for developing effective mitigation strategies.

Purpose of the Study:

• To develop and apply an exposure-based traffic assignment (TA) model for Chicago.
• To quantify PM2.5 exposure and associated damages from various vehicle types.
• To compare baseline travel equilibrium with pollution-optimized routing scenarios.

Main Methods:

• Utilized an exposure-based TA model incorporating travel demand data.
• Considered emissions from light-duty vehicles, heavy-duty trucks, public transit, and EV charging.
• Compared user equilibrium (UET) with system optimal (SOI) scenarios based on pollutant intake.

Main Results:

• Baseline PM2.5 exposure damages estimated at $3.7B-$8.3B/year.
• SOI reduced total damages by 8.2% and benefited high-impact populations (10-20% reduction).
• SOI increased travel time by 66%; bi-objective optimization and other strategies showed 1-40% exposure reduction.

Conclusions:

• Exposure-based TA modeling effectively quantifies PM2.5 impacts and evaluates mitigation strategies.
• Reducing PM2.5 exposure involves trade-offs with travel time, necessitating balanced optimization.
• Combined strategies, including cleaner fleets and increased public transit, can achieve over 50% exposure reduction.