Bioscience and Biotechnology Research Center, Guangxi Academy of Sciences, 98 Daling Road, Nanning, Guangxi, 530007, China.
Sci Rep. 2017 Apr 7;7:46216. doi: 10.1038/srep46216.
SO emissions lead to various harmful effects on environment and human health. The SO emission in China has significant contribution to the global SO emission, so it is necessary to employ various methods to study SO emissions in China with great details in order to lay the foundation for policymaking to improve environmental conditions in China. Network analysis is used to analyze the SO emissions from power generation, industrial, residential and transportation sectors in China for 2008 and 2010, which are recently available from 1744 ground surface monitoring stations. The results show that the SO emissions from power generation sector were highly individualized as small-sized clusters, the SO emissions from industrial sector underwent an integration process with a large cluster contained 1674 places covering all industrial areas in China, the SO emissions from residential sector was not impacted by time, and the SO emissions from transportation sector underwent significant integration. Hierarchical structure is obtained by further combining SO emissions from all four sectors and is potentially useful to find out similar patterns of SO emissions, which can provide information on understanding the mechanisms of SO pollution and on designing different environmental measure to combat SO emissions.
二氧化硫排放对环境和人类健康造成各种有害影响。中国的二氧化硫排放对全球的二氧化硫排放有重大贡献，因此有必要采用各种方法详细研究中国的二氧化硫排放情况，为改善中国的环境状况制定政策奠定基础。网络分析用于分析 2008 年和 2010 年中国发电、工业、住宅和交通部门的二氧化硫排放情况，这些数据最近可从 1744 个地面监测站获得。结果表明，发电部门的二氧化硫排放呈现出高度个体化的小型聚类特征，工业部门的二氧化硫排放经历了一个整合过程，包含了 1674 个地点的大型聚类，涵盖了中国所有的工业区，住宅部门的二氧化硫排放不受时间的影响，而交通部门的二氧化硫排放则经历了显著的整合。通过进一步将四个部门的二氧化硫排放结合起来，可以得到一个层次结构，这对于发现类似的二氧化硫排放模式是有用的，这些模式可以提供关于理解二氧化硫污染机制以及设计不同环境措施来控制二氧化硫排放的信息。
