The Library
Dynamic complex network analysis of PM2.5 concentrations in the UK, using hierarchical directed graphs (V1.0.0)
Tools
Tools
Tools
Broomandi, Parya, Geng, Xueyu , Guo, Weisi, Ryeol Kim, Jong , Pagani , Alessio and Topping, David (2021) Dynamic complex network analysis of PM2.5 concentrations in the UK, using hierarchical directed graphs (V1.0.0). Sustainability, 13 (4). 2201. doi:10.3390/su13042201 ISSN 2071-1050.
|
PDF
WRAP-Dynamic-complex-network-analysis-PM2.5-concentrations-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2050Kb) | Preview |
Official URL: https://doi.org/10.3390/su13042201
Abstract
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.
| Item Type: | Journal Article | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TD Environmental technology. Sanitary engineering | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
| Library of Congress Subject Headings (LCSH): | Air quality management -- Great Britain, Air -- Pollution -- Measurement, Pollutants -- Environmental aspects, Atmospheric deposition -- Great Britain, Air -- Pollution -- Mathematical models | |||||||||
| Journal or Publication Title: | Sustainability | |||||||||
| Publisher: | MDPI | |||||||||
| ISSN: | 2071-1050 | |||||||||
| Official Date: | 18 February 2021 | |||||||||
| Dates: |
|
|||||||||
| Volume: | 13 | |||||||||
| Number: | 4 | |||||||||
| Article Number: | 2201 | |||||||||
| DOI: | 10.3390/su13042201 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 3 March 2021 | |||||||||
| Date of first compliant Open Access: | 4 March 2021 | |||||||||
| RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
