Version 1
: Received: 28 November 2023 / Approved: 28 November 2023 / Online: 28 November 2023 (16:15:34 CET)
How to cite:
Zhao, Z.; Xue, D.; Sun, H.; Wang, W.; Ying, N. Network Approach Identifies the Control Cities of O3 Pollution. Preprints2023, 2023111827. https://doi.org/10.20944/preprints202311.1827.v1
Zhao, Z.; Xue, D.; Sun, H.; Wang, W.; Ying, N. Network Approach Identifies the Control Cities of O3 Pollution. Preprints 2023, 2023111827. https://doi.org/10.20944/preprints202311.1827.v1
Zhao, Z.; Xue, D.; Sun, H.; Wang, W.; Ying, N. Network Approach Identifies the Control Cities of O3 Pollution. Preprints2023, 2023111827. https://doi.org/10.20944/preprints202311.1827.v1
APA Style
Zhao, Z., Xue, D., Sun, H., Wang, W., & Ying, N. (2023). Network Approach Identifies the Control Cities of O3 Pollution. Preprints. https://doi.org/10.20944/preprints202311.1827.v1
Chicago/Turabian Style
Zhao, Z., Weiping Wang and Na Ying. 2023 "Network Approach Identifies the Control Cities of O3 Pollution" Preprints. https://doi.org/10.20944/preprints202311.1827.v1
Abstract
In recent years, ozone (O3) pollution has been rapidly spreading, restricting further improvement of air quality in China. Investigating the interaction of O3 concentration and identifying their driven cities are important for the prevention and control of O3 pollution in China. However, the complex interaction between O3 pollution between the cities and their driven cities has not yet been revealed. In this study, we address this gap based on complex network methods. Specifically, an ozone relational network is constructed using an association calculation method. The driven nodes and spatial clusters were analyzed based on the maximum matching algorithm and the Louvain algorithm. The findings of the study reveal an aggregation phenomenon in the ozone network concerning distance. Furthermore, as the threshold Tc varies, the proportion of driven nodes exhibits a positive correlation. Moreover, a closer threshold value corresponds to a higher coincidence ratio of the driven nodes. The results provide scientific guidance for national O3 pollution prevention and regional synergy formatting. Furthermore, the introduced network-based approaches offer a mythological framework for the study of air pollution in key cities and clusters.
Keywords
ozone; complex network; maximum matching algorithm; community classification
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
