摘要
以大气污染物和温室气体协同控制为出发点,以天津市为例,选取经济、社会、环境三方面共21个影响城市大气主要污染物与CO_2协同减排的因素,运用人工神经网络方法,辨识影响SO_2、NO_X、CO_2排放的关键性影响因素,并针对各因素的重要程度提出协同控制城市大气污染物与二氧化碳排放的措施建议,从而不断提高环境管理决策的科学性和可行性,为我国城市实现SO_2、NO_X、CO_2的协同减排提供有力的理论支撑。
Based on the coordinated control of air pollutants and greenhouse gases in Tianjin, this paper selected 21 factors that affect synergistic urban air pollutants and CO2 emission reduction from the aspects of economy, society and environment to identify the key factors in SO2, NOX and CO2 emission by artificial neural network method. At the same time, measures and suggestions controlling urban air pollutants and carbon dioxide emission were cooperatively put forward in the study to show the importance of various factors. Thus, it could improve the scientific nature and feasibility of environmental management decisions continuously and provide strong theoretical support for the cities in our country to realize co-reduction of SO2, NOX and CO2.
出处
《城市环境与城市生态》
CAS
2016年第2期17-20,共4页
Urban Environment & Urban Ecology
基金
天津市科技发展战略研究计划项目"京津冀大气环境协同改善决策支持系统研究"(14ZLZLZF00068)
中国清洁发展机制基金赠款项目"天津市应对气候变化‘十三五’规划思路研究"(H-201603-Z-G-004)
关键词
协同减排
人工神经网络方法
关键影响因素
synergistic emission reduction
artificial neural network method
key influencing factor
