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Air pollution caused by PM 2.5 is a significant public health concern, with vehicle emissions being a major contributor. Previous research focused on assessing personal exposure in local traffic microenvironments, but this approach is resource-intensive and challenging to implement city-wide. Traditional methods of assessing population exposure have limitations in reflecting population mobility and spatiotemporal heterogeneity. This study presents a method for obtaining high-resolution spatiotemporal distributions of the population and their exposure to traffic-related PM 2.5 in Guangzhou using multisource data. The results indicate that the simulated population distribution was consistent with census data and allow identification of population density hotspots. Meanwhile, this study also examines 24-hour population changes for dynamic high-resolution assessment of population exposure. The findings show that low-income individuals and rural-urban migrants residing in urban villages have a mean population exposure exceeding 5.18 μg/min, surpassing the PM 2.5 exposure of commuters in enclosed cabins. This emphasizes the urgent need to mitigate the negative impacts of traffic-related PM 2.5 on public health. [ABSTRACT FROM AUTHOR]