Dynamic Complex Network Analysis of PM2.5 Concentrations in the UK, Using Hierarchical Directed Graphs (V1.0.0)
Dynamic Complex Network Analysis of PM2.5 Concentrations in the UK, Using Hierarchical Directed Graphs (V1.0.0)
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DOI：
10.3390/su13042201
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发表时间：
2021-02-01
期刊：
影响因子：
3.9
通讯作者：
Kim, Jong Ryeol
中科院分区：
文献类型：
--
作者：
Broomandi, Parya;Geng, Xueyu;Kim, Jong Ryeol
广泛接触大气中的细颗粒物会增加患各种呼吸道和心脏疾病的风险，因为这些细颗粒物能够深入血液和肺部。在全球范围内，在欧洲和其他地方进行的流行病学研究提供了证据基础，表明PM2.5的主要作用每年导致400多万人死亡。传统的方法模拟具有高维数的颗粒物在大气中的传输和化学反应过程，很难进行详尽的因果推理。替代模型简化方法，特别是数据驱动的有向图表示，推导因果方向性和空间嵌入。利用英国14个城市一年的PM2. 5浓度数据，建立了无向相关和有向格兰杰因果网络。为了证明这两种简化的情况下，英国分为两个南部和北方连接的城市社区，在夏季和春季显着的空间嵌入。它继续达到稳定的干扰，通过网络营养相干参数和冬季被解释为最可观的脆弱性。由于我们新颖的图简化建模，我们可以表示高维知识的因果推理和稳定性框架。
The risk of a broad range of respiratory and heart diseases can be increased by widespread exposure to fine atmospheric particles on account of their capability to have a deep penetration into the blood streams and lung. Globally, studies conducted epidemiologically in Europe and elsewhere provided the evidence base indicating the major role of PM2.5 leading to more than four million deaths annually. Conventional approaches to simulate atmospheric transportation of particles having high dimensionality from both transport and chemical reaction process make exhaustive causal inference difficult. Alternative model reduction methods were adopted, specifically a data-driven directed graph representation, to deduce causal directionality and spatial embeddedness. An undirected correlation and a directed Granger causality network were established through utilizing PM2.5 concentrations in 14 United Kingdom cities for one year. To demonstrate both reduced-order cases, the United Kingdom was split up into two southern and northern connected city communities, with notable spatial embedding in summer and spring. It continued to reach stability to disturbances through the network trophic coherence parameter and by which winter was construed as the most considerable vulnerability. Thanks to our novel graph reduced modeling, we could represent high-dimensional knowledge in a causal inference and stability framework.