Constructing a Near Real-time Space-time Cube to Depict Urban Ambient Air Pollution Scenario
Constructing a Near Real-time Space-time Cube to Depict Urban Ambient Air Pollution Scenario
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DOI：
10.1111/j.1467-9671.2011.01283.x
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发表时间：
2011-10-01
影响因子：
2.4
通讯作者：
Lu, Yongmei
中科院分区：
文献类型：
--
作者：
Fang, Tianfang B.;Lu, Yongmei
本研究采用近实时时空立方体方法来描绘一个跨越时空的动态城市空气污染情景。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整合到三维空间中的方法。立方体的底部代表二维地理空间中空气污染的变化，而高度代表时间。这样，污染随时间的变化可以通过立方体的不同组件层来描述。本文采用时空空气污染立方体模型对德克萨斯州休斯敦市日环境臭氧（O-3）污染进行了模拟。采用土地利用回归（LUR）建模和空间插值两种方法构建空气污染立方体的逐时分量层。结果表明，LUR模型对大气污染水平的预测效果优于空间插值模型。随着实时空气污染数据的可用性，该方法可以扩展到生成实时空气污染立方体，以便更准确地跨空间和时间测量空气污染，为流行病学、健康地理学和环境监管等研究提供重要支持。
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.