An exposure-based
traffic assignment (TA) model is used to quantify
primary and secondary fine particulate matter (PM2.5) exposure
from on-road vehicle flow on the Chicago Metropolitan Area regional
network. PM2.5 exposure due to emissions from light-duty
vehicles, heavy-duty trucks, public transportation, and electricity
generation for electric vehicle charging and light-rail transportation
is considered. The model uses travel demand data disaggregated by
time-of-day period and vehicle user class to compare the exposure
impacts of two TA optimization scenarios: a baseline user equilibrium
with respect to travel time (UET) and a system optimal with respect
to pollutant intake (SOI). Estimated baseline PM2.5 exposure
damages are $3.7B–$8.3B/year. The SOI uses exposure-based vehicle
rerouting to reduce total damages by 8.2%, with high-impacted populations
benefiting from 10% to 20% reductions. However, the SOI’s rerouting
principle leads to a 66% increase in travel time. The model is then
used to quantify the mitigation potential of different exposure reduction
strategies, including a bi-objective optimization formulation that
minimizes travel time and PM2.5 exposure concurrently,
adoption of a cleaner vehicle fleet, higher public transportation
use, particle filtration, and exposure-based truck routing. Exposure
reductions range between 1% and 40%, but collective adoption of all
strategies would lead to reductions upward of 50%.