Fine particulate matter (PM2.5) is harmful to human health. Although the relationship between urban land use and PM2.5 has been studied in recent years, there has been little consideration of the relationship between land use structure and PM2.5 spatiotemporal patterns at the microscale. Based on mobile monitoring PM2.5 data and point of interest data, this paper explored their relationship with a classification and regression tree model. The results showed that PM2.5 exhibits spatiotemporal heterogeneity at the microscale. The neighborhoods’ land use structure can explain 60.4% of the PM2.5 spatiotemporal patterns. Transportation and ecology are the two most significant land use types that correlated with PM2.5 spatiotemporal patterns. Fourteen rules of neighborhood land use structures with different land use types are identified land use structure which leads to different spatiotemporal patterns of PM2.5. The higher the PM2.5 risk, the stronger the correlation with neighborhood land use structure is. The classification and regression tree model can be effectively used to judge the relationship between neighborhood land use structure and PM2.5 spatiotemporal patterns. The results provide a basis for developing appropriate measures, based on local conditions, to predict PM2.5 pollution levels at the microscale, and reduce the risk of neighborhood exposure to PM2.5.

Previous behavioral studies on urban structure have been limited by the scale, accuracy, or promptness in obtaining statistical data used for delimiting retail center boundaries and hierarchical analysis. Using a large amount of GPS-enabled taxi data from Guangzhou, China, this research attempts to delimit the boundaries of retailing centers and explore their hierarchical characteristics. The identified retailing centers are verified with economic census data, and the retailing hierarchical structure is identified through assessing trip summation, travel distance, and travel time. Among these indicators, trip summation reveals hierarchical characteristics best. The urban retailing hierarchical structure also reveals the centrality of urban Guangzhou.

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