Assessing age-stratified pedestrian exposure to PM₁ using street view imagery and mobile monitoring: A machine learning approach

Protecting pedestrians from air pollution requires understanding how exposure varies across age groups, yet most regional-scale pedestrian exposure studies lack demographic stratification. This challenge is compounded for PM₁, which remains underexplored despite evidence of heightened health risks compared to larger particles. In this study, we develop an age-stratified, spatially explicit exposure framework integrating mobile-monitored PM₁ concentrations with deep learning-derived age-stratified pedestrian volume from street-view imagery. A transfer learning-based model automatically classifies pedestrians into three age groups (children, adults, elderly), enabling large-scale exposure estimation across urban sidewalks. Tree-based machine learning models achieve R² values of 80.9% for PM₁ concentrations and 64.38%-79.26% for age-stratified exposure prediction. Variable interpretation analysis reveals distinct determinants: ambient PM₁ is primarily driven by urban morphology and meteorology, whereas pedestrian exposure is governed by points of interest distributions reflecting age-specific destination patterns. Age-specific exposure analysis demonstrates that high-pollution zones do not necessarily coincide with high-exposure locations for vulnerable populations, indicating that pollution reduction alone is insufficient without considering demographic-specific activity patterns. Furthermore, the differential importance of points of interest across age groups provides compelling evidence of distinct activity spaces and destination preferences throughout the life course. The proposed framework enables identification of critical intervention zones for each age group, supporting evidence-based urban planning strategies for equitable exposure mitigation.