基于伴随模式的典型PM_(2.5)和O_(3)双高污染事件减排措施  

Emission reduction measures for typical PM_(2.5)and O_(3)co-pollution event based on the adjoint model

作　　者：刘哲 安兴琴[1] 王超[2] 李江涛 LIU Zhe;AN Xing-qin;WANG Chao;LI Jiang-tao(State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing,100081,China;CMA Earth System Modeling and Prediction Centre,China Meteorological Administration,Beijing,100081,China)

机构地区：[1]中国气象科学研究院气象影响与风险研究中心,北京100081 [2]中国气象局地球系统数值预报中心,北京100081

出　　处：《中国环境科学》2024年第12期6559-6568,共10页China Environmental Science

基　　金：国家重点研发计划(2022YFC3701205);国家自然科学基金资助项目(41975173);中国气象科学研究院科学技术发展基金(2021KJ011)。

摘　　要：为了探讨PM_(2.5)和O_(3)双高污染及其有效控制措施,运用GRAPES-CUACE伴随模式对2019年4月19~25日北京市的一次典型双高污染事件进行“源-浓度”敏感性分析,定量评估了本地及周边地区前体物排放对北京市24h平均PM_(2.5)(24-hr PM_(2.5))和MDA8O_(3)浓度峰值的贡献,并利用伴随模式开展相应的减排措施试验.伴随敏感性分析结果表明,此次北京市双高污染事件的24-hr PM_(2.5)和MDA8O_(3)浓度峰值受到本地及周边地区的前体物排放的共同影响.一次PM_(2.5)(PPM_(2.5))排放源对24-hr PM_(2.5)浓度峰值的主要贡献在前48h,其中河北源贡献最大(49.7%),其次是山东源(24.4%)和北京源(20.1%).O_(3)的生成由VOCs控制,NO_(x)和VOCs排放源的主要贡献时段分别为前30h和前38h.其中河北贡献最大,NO_(x)和VOCs分别贡献了27.0%和23.8%,北京源(20.9%和4.9%)次之.双高污染的减排试验结果显示,当北京市24-hr PM_(2.5)浓度峰值达标时,NO_(x)、VOCs和PPM_(2.5)减排比例相近,各省市的减排力度依次为:河北(55.8%、59.1%和61.3%)、北京(60.0%,47.4%和60.4%)、山东(44.0,51.2%和61.3%)、天津(42.7%,42.7%和42.7%)及山西(44.0%,40.9%和42.7%).而MDA8O_(3)浓度峰值在迭代过程中先上升后下降,达标时需减排较多NO_(x)和VOCs.各省市减排力度依次为河北(67.8%和67.1%)、北京(66.0%和56.3%)、山东(57.3%和59.5%)、天津(50.9%和52.4%)及山西(55.4%和46.0%).To investigate PM_(2.5)and O_(3)co-pollution and its effective control measures,the"source-concentration"sensitivity analysis of a typical co-pollution event in Beijing from April 19 to 25,2019 was conducted by GRAPES-CUACE adjoint model in this paper.The contribution of local and surrounding precursor emissions to the peak concentrations of 24h average PM_(2.5)(24-hr PM_(2.5))and MDA8O_(3)in Beijing was quantitatively assessed,and corresponding emission reduction experiments were conducted using the adjoint model.The results of the adjoint sensitivity analysis indicated that the peak concentrations of 24-hr PM_(2.5)and MDA8O_(3)in Beijing were jointly influenced by the precursor emissions from both local and surrounding areas.The peak 24-hr PM_(2.5)concentrations were mainly contributed by primary PM_(2.5)(PPM_(2.5))emission sources within the preceding 48h,with the largest contribution from Hebei(49.7%),followed by Shandong(24.4%)and Beijing(20.1%).The formation of O_(3)was controlled by VOCs.The primary contribution periods were within the first 30h for NO_(x)and the first 38h for VOCs.Hebei made the largest contributions,with NO_(x)and VOCs contributing 27.0%and 23.8%,respectively,followed by Beijing(20.9%and 4.9%).The results of the emission reduction experiments for co-pollution event showed that when the peak 24-hr PM_(2.5)concentrations in Beijing met the standard,the reduction percentages of NO_x,VOCs and PPM_(2.5)were similar,with the reduction percentages for each province as follows:Hebei(55.8%,59.1%,and 61.3%),Beijing(60.0%,47.4%,and 60.4%),Shandong(44.0%,51.2%,and 61.3%),Tianjin(42.7%,42.7%,and42.7%),and Shanxi(44.0%,40.9%,and 42.7%).The peak MDA8O_(3)concentrations initially increased and then decreased during the iterative process,and more NO_(x)and VOCs needed to be reduced when reaching the standard.The emission reduction percentages for local and surrounding areas were as follows:Hebei(67.8%and 67.1%),Beijing(66.0%and 56.3%),Shandong(57.3%and 59.5%),Tianjin(50.9%and 52.4%),and Shanxi(55.4%and 4

关 键 词：伴随模式 PM_(2.5)和O_(3)双高污染 敏感性分析 污染控制 北京 

分 类 号：X513[环境科学与工程—环境工程]