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Centrality metric for the vulnerability of urban networks to toxic releases
Physical Review E ( IF 2.4 ) Pub Date : 2020-03-31 , DOI: 10.1103/physreve.101.032312
Sofia Fellini , Pietro Salizzoni , Luca Ridolfi

The dispersion of airborne pollutants in the urban atmosphere is a complex, canopy-driven process. The intricate structure of the city, the high number of potential sources, and the large spatial domain make it difficult to predict dispersion patterns, to simulate a great number of scenarios, and to identify the high-impact emission areas. Here we show that these complex transport dynamics can be efficiently characterized by adopting a complex network approach. The urban canopy layer is represented as a complex network. Street canyons and their intersections shape the spatial structure of the network. The direction and the transport capacity of the flow in the streets define the direction and the weight of the links. Within this perspective, pollutant contamination from a source is modeled as a spreading process on a network, and the most dangerous areas in a city are identified as the best spreading nodes. To this aim, we derive a centrality metric tailored to mass transport in flow networks. By means of the proposed approach, vulnerability maps of cities are rapidly depicted, revealing the nontrivial relation between urban topology, transport capacity of the street canyons, and forcing of the external wind. The network formalism provides promising insight in the comprehensive analysis of the fragility of cities to air pollution.

中文翻译:

城市网络易受有毒物质排放危害的集中度指标

空气中污染物在城市大气中的扩散是一个复杂的,由树冠驱动的过程。城市的错综复杂的结构,大量的潜在来源以及广阔的空间范围,使得难以预测散布模式,模拟大量情景以及识别高影响排放区域。在这里,我们表明可以通过采用复杂的网络方法来有效地表征这些复杂的传输动力学。城市树冠层表示为一个复杂的网络。街道峡谷及其交叉口塑造了网络的空间结构。街道中水流的方向和输送能力决定了路段的方向和重量。从这个角度来看,源头的污染物污染被建模为网络上的传播过程,以及城市中最危险的区域被确定为传播最充分的节点。为此,我们推导出了针对流量网络中的大众运输量身定制的中心度度量标准。通过提出的方法,快速描绘了城市的脆弱性图,揭示了城市拓扑结构,街道峡谷的运输能力和外部风的强迫之间的重要关系。网络形式主义为全面分析城市对空气污染的脆弱性提供了有希望的见解。和强迫外部风。网络形式主义为全面分析城市对空气污染的脆弱性提供了有希望的见解。和强迫外部风。网络形式主义为全面分析城市对空气污染的脆弱性提供了有希望的见解。
更新日期:2020-03-31
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