Abstract
AbstractThis study adopts a near real‐time space‐time cube approach to portray a dynamic urban air pollution scenario across space and time. Originating from time geography, space‐time cubes provide an approach to integrate spatial and temporal air pollution information into a 3D space. The base of the cube represents the variation of air pollution in a 2D geographical space while the height represents time. This way, the changes of pollution over time can be described by the different component layers of the cube from the base up. The diurnal ambient ozone (O3) pollution in Houston, Texas is modeled in this study using the space‐time air pollution cube. Two methods, land use regression (LUR) modeling and spatial interpolation, were applied to build the hourly component layers for the air pollution cube. It was found that the LUR modeling performed better than the spatial interpolation in predicting air pollution level. With the availability of real‐time air pollution data, this approach can be extended to produce real‐time air pollution cube is for more accurate air pollution measurement across space and time, which can provide important support to studies in epidemiology, health geography, and environmental regulation.
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