Commuting Challenges for Big Cities: How to Tackle Particulate Matter Human Exposure?

Abstract

Air quality in large cities is a global public health issue that has received increasing attention in recent decades. The dynamics of atmospheric pollutant concentrations form a complex system influenced by meteorology, topography, emission sources, and fleet composition, interacting in a nonlinear pattern. We monitored particulate matter (PM) in two transport modes (walking and bus) and a fixed point in Curitiba, Brazil, to understand human exposure. Real-time ${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{10}$ concentrations, with GPS data, were collected using low-cost sensors and microcontrollers. Machine learning models identified key pollution drivers. Results showed that mean PM concentrations were higher inside buses. Afternoon bus samples frequently exceeded 45 $\mu g$/$m^3$ for ${\mathrm{PM}}_{10}$, surpassing WHO daily limits, while the highest walking peak (approximately 34–35 $\mu g$/$m^3$) occurred near a major highway. ${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{10}$ were strongly correlated, especially for walking (R = 0.91). Despite higher concentrations inside buses, cumulative exposure and inhaled dose were greater while walking, with average ${\mathrm{PM}}_{2.5}$ exposure of 0.18 $\mu g$ ${\min }^{-1}$ (vs. 0.11 $\mu g$ ${\min }^{-1}$ on buses) and average inhaled doses of 5.74 $\mu g$ (walking) versus 3.05 $\mu g$ (bus). Longer commute duration and increased inhalation rates explain this difference. Random Forest (RF) analysis identified boundary layer height as the most important predictor of ${\mathrm{PM}}_{2.5}$, followed by relative humidity and the number of SUVs. These findings highlight how meteorology, fleet composition, and travel mode shape exposure and support strategies for healthier and sustainable urban mobility.