摘要
与其他地区相比,东北地区冬季低温漫长,有其非道路移动源大气污染物排放清单的独自特征.本文将东北地区分成辽宁省城市群和哈长城市群进行分析.首先,基于《非道路移动源大气污染物排放清单编制技术指南》(试行)中的排放因子法建立非道路移动源排放清单,分析其排放以及时空分布特征.其次,结合相关政策目标基于情景分析预测2030年的排放.最后,提出合理的减排建议.结果表明:1)东北地区非道路移动源PM 10,PM 2.5,NO x,THC和CO排放量分别是13.0×10^(3),12.5×10^(3),205.6×10^(3),37.0×10^(3)和101.1×10^(3)t;2)两城市群工程机械占比最大,分别为44.5%和44.8%;3)在基准控制情景和强化控制情景下,总体减排效果可提高50%以上.
Compared with other regions,Northeast China has its unique characteristics of non-road mobile source emission inventories due to its long and cold winter.In this paper,the analysis was divided into Liaoning Province and Harbin-Changchun Megalopolis.Firstly,the emission inventory of non-road mobile sources was established based on technical guide for preparation of air pollutant emission list of non-road mobile sources(trial),and its emission and spatial-temporal distribution characteristics were analyzed.Secondly,the emissions in 2030 were forecasted based on the scenario analysis,taking into account of the relevant policy objectives.Finally,reasonable emission reduction recommendations were made.The results show that:1)the emissions of PM 10,PM 2.5,NO x,THC,and CO were 13.0×10^(3),12.5×10^(3),205.6×10^(3),37.0×10^(3)and 101.1×10^(3)t,respectively;2)construction machinery accounts for the largest share of emissions in the two urban agglomerations with 44.5%and 44.8%,respectively;3)the overall emission reduction can be improved by more than 50%under both the baseline scenario and the enhanced control scenario.
作者
高成康
由焕
巴乔
梁程序
GAO Cheng-kang;YOU Huan;BA Qiao;LIANG Cheng-xu(School of Metallurgy,Northeastern University,Shenyang 110819,China;SEP Key Laboratory of Eco-industry,Northeastern University,Shenyang 110819,China;Midea Group Life Electrical Business Unit,Foshan 528000,China)
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2021年第3期358-366,共9页
Journal of Northeastern University(Natural Science)
基金
国家重点研发计划项目(2017YFC0212303-03)
国家自然科学基金资助项目(41871212).
关键词
非道路移动源
大气污染物
排放清单
时空特征
减排情景预测
non-road mobile source
air pollution
emission inventory
spatial-temporal characteristics
emission reduction scenario prediction
