Dynamic assessment of population exposure to traffic-originated PM2.5 based on multisource geo-spatial data

Air pollution caused by PM2.5 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 citywide. 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 trafficrelated PM2.5 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 mu g/min, surpassing the PM2.5 exposure of commuters in enclosed cabins. This emphasizes the urgent need to mitigate the negative impacts of traffic-related PM2.5 on public health.

Automated summary

This study presents a method for obtaining high-resolution spatiotemporal distributions of population and their exposure to traffic-related PM2.5 in Guangzhou, China. The results indicate population density hotspots and reveal that low-income individuals and rural-urban migrants have higher exposure to PM2.5 compared to commuters in enclosed cabins. These findings emphasize the urgent need to mitigate the negative impacts of traffic-related PM2.5 on public health.