Disentangling Near-Road Emission Inequities in Hong Kong through Data-Driven Spatiotemporal Traffic Dynamics

Traffic emissions contribute disproportionately to exposure inequities in dense cities. Delivery fleets and public buses can intensify these burdens in vulnerable communities, yet most assessments overlook such dynamics by aggregating fleets into broad categories and averaging across the day. This obscures when and which vehicles drive inequities. We developed a vehicle-class and hour-resolved approach for Hong Kong to estimate traffic emissions. We integrated high-resolution traffic counts, street imagery, and detector data with machine learning and computer vision to model hourly NO_<i>x</i> and PM_2.5 for all road segments during typical daily activity hours. The framework explains over 95% of the variance in NO_<i>x</i> and PM_2.5 emissions. Results show substantial traffic-related emission inequities in Hong Kong, with low-income residents experiencing 8%-9% higher NO_<i>x</i> levels than high-income residents, and Chinese residents experiencing 40%-52% higher NO_<i>x</i> levels than White residents. The dominant contributors shift over the day, with delivery fleets driving daytime inequities and franchised buses amplifying evening inequities. Across all hours, light-duty goods vehicles contribute 31-35% of disparities, franchised buses 25-35%, and heavy-duty goods vehicles 19-23%, varying by population group. This study provides one of the first data-driven analyses of vehicle-specific impacts, revealing when and which vehicles drive inequities and guiding equity-focused interventions.