雾霾污染的区域结构特征学习

Structure learning of region feature of haze pollution

基于复杂网络分析方法,开展雾霾污染结构特征学习。收集了中国363个城市2015-2018年每小时的PM2.5实时观测数据,分析了4年中各城市PM2.5变化,利用复杂网络图建模,研究了城市之间形成的雾霾污染网络的中心点和社区结构的变化。研究发现:经过雾霾治理,全国范围内雾霾有明显改善,北京及东北地区雾霾污染治理效果优于西北地区;雾霾污染网络具有中心点,中心点大多为污染严重的地区,主要分布于中西部地区,开展雾霾治理需要重点关注污染网络的中心点城市及其所在区域;雾霾污染网络存在社区结构,社区结构与地理位置高度一致,不同社区结构之间雾霾污染的成因及特征有一定的差异,开展雾霾治理不仅要考虑不同社区的差异,而且同一个社区内部要相互配合,协作治理,才能取得更好的雾霾治理效果。

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.