Multi-Perspective Analysis and Spatiotemporal Mapping of Air Pollution Monitoring Data

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• 作者：Alexander Kolovos ; Andr Skupin ; Michael Jerrett ; George Christakos
• 刊名：Environmental Science & Technology
• 出版年：2010
• 出版时间：September 1, 2010
• 年：2010
• 卷：44
• 期：17
• 页码：6738-6744
• 全文大小：370K
• 年卷期：v.44,no.17(September 1, 2010)
• ISSN：1520-5851

文摘

Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty; it also leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian maximum entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly averaged NO₂ and mean annual SO₄ measurements in California over the 15-year period 1988−2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.