Abstract：
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° × 0.3125° 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 O3 concentrations ≥160 μg 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 μg m-3 and 0.96 μg 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.
摘要：
虽然自2013年实施《大气污染防治行动计划》以来，长江三角洲5种基本环境空气污染物浓度有所降低，但臭氧浓度仍在增加。为了探讨YRD臭氧污染的原因，我们使用GEOS-Chem及其伴随模型研究了典型循环模式下重臭氧污染事件中臭氧对不同源区和排放部门的前体排放的敏感性。该模型采用清华大学中国多分辨率排放清单（MEIC）和0.25°×0.3125°嵌套网格。通过使用T模式主成分分析(T-PCA)，2013年至2019年位于YRD中心区域的南京市重度臭氧污染日（观测到的MDA8O3浓度≥160μgm-3）的循环模式分为四种类型，具有西伯利亚低地的主要特征，巴尔哈什湖高，东北低,黄海高,和表面的东南风。伴随结果表明，江苏和浙江的排放对南京市重度臭氧污染的贡献最大。江苏省人为NOx和NMVOCs排放量减少10%，浙江和上海可以将南京的臭氧浓度分别降低3.40μgm-3和0.96μgm-3。然而,南京当地NMVOCs排放的减少对臭氧浓度影响不大，减少局部NOx排放甚至会增加臭氧污染。对于不同的排放部门，工业排放占南京市臭氧污染的31%-74%，其次是交通排放（18%-49%）。该研究可为预测臭氧污染事件和制定准确的减排策略提供科学依据。
