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大城市机动车污染物排放与控制的情景预测

吉木色 郭秀锐 郎建垒 陈东升

吉木色, 郭秀锐, 郎建垒, 陈东升. 大城市机动车污染物排放与控制的情景预测[J]. 环境科学研究, 2013, 26(9): 919-928.
引用本文: 吉木色, 郭秀锐, 郎建垒, 陈东升. 大城市机动车污染物排放与控制的情景预测[J]. 环境科学研究, 2013, 26(9): 919-928.
JI Mu-se, GUO Xiu-rui, LANG Jian-lei, CHEN Dong-sheng. Scenario Prediction of Motor Vehicle Emission and Control in Megacities[J]. Research of Environmental Sciences, 2013, 26(9): 919-928.
Citation: JI Mu-se, GUO Xiu-rui, LANG Jian-lei, CHEN Dong-sheng. Scenario Prediction of Motor Vehicle Emission and Control in Megacities[J]. Research of Environmental Sciences, 2013, 26(9): 919-928.

大城市机动车污染物排放与控制的情景预测

基金项目: 国家自然科学基金项目(51208010)

Scenario Prediction of Motor Vehicle Emission and Control in Megacities

  • 摘要: 机动车排放控制措施的有效实施对改善城市大气环境质量具有重要意义. 以北京市为例,利用情景预测法评估2011—2020年各项控制措施对城市机动车常规污染物(CO、NOx、HC、PM10)的削减效果.建立Gompertz模型并估算动态车龄分布以预测机动车保有量,运用排放因子法估算6种机动车排放控制情景的污染物削减量. 结果表明:与基准情景相比,轻型客车保有量调控情景对CO、HC和PM10的削减效果较显著,在2020年可分别削减7.81%、9.88%和5.78%;排放标准更新情景对4种污染物均能有效削减,尤其是对NOx和PM10,可分别削减21.19%和24.67%;而淘汰高排放机动车情景的短期削减效果显著,但中、长期效果较差;新能源车推广情景因受到经济、技术条件的限制,削减效果较弱;综合情景考虑了以上所有的削减控制措施并达到最大的削减效果,2020年对CO、NOx、HC和PM10的削减率分别达到29.45%、42.54%、28.04%和41.30%,与基准年(2010年)相比,分别削减约2.81×105、0.63×104、3.77×104和0.17×104t.

     

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出版历程
  • 收稿日期:  2013-02-25
  • 修回日期:  2013-05-23
  • 刊出日期:  2013-09-25

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