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Constructing a Near Real-time Space-time Cube to Depict Urban Ambient Air Pollution Scenario

机译：构造近实时时空立方体以描述城市环境空气污染情景

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摘要

This 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 (O_3) 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.
机译：这项研究采用了近实时的时空立方体方法来描绘跨时空动态的城市空气污染情景。时空立方体起源于时间地理学，提供了一种将时空空气污染信息集成到3D空间中的方法。立方体的底部表示2D地理空间中空气污染的变化，而高度表示时间。这样，污染随时间的变化可以通过从下到上的多维数据集的不同组成层来描述。本研究使用时空空气污染立方体模拟了德克萨斯州休斯敦的昼夜环境臭氧（O_3）污染。应用了两种方法，即土地利用回归（LUR）建模和空间插值法来建立空气污染多维数据集的每小时组成层。发现在预测空气污染水平方面，LUR模型的性能优于空间插值。利用实时空气污染数据，可以扩展此方法以生成实时空气污染多维数据集，以便跨时空更准确地测量空气污染，这可以为流行病学，健康地理学和环境法规。

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