ZHANG Hai*, WANG Shenghan, GUO Xiao
(School of Mathematics, Northwest University, Xi′an 710127, Shaanxi, China)
Abstract:
The region structure learning of haze pollution is studied by using the complex network analysis method. The PM2.5 data of each hour from 2015 to 2018 in 363 cities of China are collected. Then the change of concentration of PM2.5 in those cities in the recent four years is analyzed. Based on the complex graphical model method, the change of hubs and structure of the haze pollution network among the 363 cities is studied. The results show that: after the haze governance, the effect of nationwide haze control has been significantly improved, but the haze control effect in Beijing and northeast China is better than that in northwest China. Haze control needs to focus on the central cities and the regions that they locate in; to carry out haze governance, we should not only consider the differences among different communities, but also cooperate with each other within the same community to achieve better haze governance results.
KeyWords:
haze pollution; community structure; big data analysis; complex network modeling; PM2.5 自1978年以来,中国经济保持了三十多年的高速增长。中国的发电量2017年达到25万亿千瓦,随着能源规模的不断扩大,伴随而来的是2013年以来的雾霾天气频发,其不仅对全国人民的生活、健康、出行产生了极大的危害,同时对中国的经济增长质量和国家形象均产生了负面的影响。因此,开展雾霾治理,制定科学合理的雾霾治理政策,对于实现“绿水青山”的现代化中国有着重要的意义。