Sensitivities of ozone to its precursors during heavy ozone pollution events in the Yangtze River Delta using the adjoint method

被引:2
作者
Mao, Yu-Hao [1 ,2 ]
Shang, Yongjie [1 ]
Liao, Hong [1 ,2 ]
Cao, Hansen [3 ]
Qu, Zhen [3 ]
Henze, Daven K. [3 ]
机构
[1] Nanjing Univ Informat Sci & Technol NUIST, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Sch Environm Sci & Engn, Jiangsu Key Lab Atmospher Environm Monitoring & Po, Nanjing 210044, Peoples R China
[2] NUIST, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, Int Joint Res Lab Climate & Environm Change ILCEC, Key Lab Meteorol Disaster,Minist Educ KLME, Nanjing 210044, Peoples R China
[3] Univ Colorado, Dept Mech Engn, Boulder, CO 80309 USA
基金
中国博士后科学基金; 国家自然科学基金重大项目;
关键词
Heavy ozone pollution; Precursor emissions; Source attribution; Adjoint method; Circulation patterns; BEIJING-TIANJIN-HEBEI; LONG-TERM TREND; TROPOSPHERIC OZONE; CIRCULATION PATTERNS; SOURCE APPORTIONMENT; WEATHER PATTERNS; NOX EMISSIONS; CHINA; REGION; MODEL;
D O I
10.1016/j.scitotenv.2024.171585
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Although the concentrations of five basic ambient air pollutants in the Yangtze River Delta (YRD) have been reduced since the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013, the ozone concentrations still increase. In order to explore the causes of ozone pollution in YRD, we use the GEOS-Chem and its adjoint model to study the sensitivities of ozone to its precursor emissions from different source regions and emission sectors during heavy ozone pollution events under typical circulation patterns. The Multi-resolution Emission Inventory for China (MEIC) of Tsinghua University and 0.25 degrees x 0.3125 degrees nested grids are adopted in the model. By using the T-mode principal component analysis (T-PCA), the circulation patterns of heavy ozone pollution days (observed MDA8 O-3 concentrations <= 160 mu g m(-3)) in Nanjing located in the center area of YRD from 2013 to 2019 are divided into four types, with the main features of Siberian Low, Lake Balkhash High, Northeast China Low, Yellow Sea High, and southeast wind at the surface. The adjoint results show that the contributions of emissions emitted from Jiangsu and Zhejiang are the largest to heavy ozone pollution in Nanjing. The 10 % reduction of anthropogenic NOx and NMVOCs emissions in Jiangsu, Zhejiang and Shanghai could reduce the ozone concentrations in Nanjing by up to 3.40 mu g m(-3) and 0.96 mu g m(-3), respectively. However, the reduction of local NMVOCs emissions has little effect on ozone concentrations in Nanjing, and the reduction of local NOx emissions would even increase ozone pollution. For different emissions sectors, industry emissions account for 31 %-74 % of ozone pollution in Nanjing, followed by transportation emissions (18 %-49 %). This study could provide the scientific basis for forecasting ozone pollution events and formulating accurate strategies of emission reduction.
引用
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页数:10
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