Abstract：
Understanding the causes and sources responsible for severe fine particulate matter (PM2.5) pollution episodes that occur under conducive synoptic weather patterns (SWPs) is essential for regional air quality management. The Yangtze River Delta (YRD) region in eastern China has experienced recurrent severe PM2.5 episodes during the winters from 2013 to 2017. In this study, we employed an objective classification approach, the self-organizing map, to investigate the underlying impact of predominant SWPs on PM2.5 pollution in the YRD. We further conducted a series of source apportionment simulations using the Particulate Source Apportionment Technology (PSAT) tool integrated within the Comprehensive Air Quality Model with Extensions (CAMx) to quantify the source contributions to PM2.5 pollution under different SWPs. Here we identified six predominant SWPs over the YRD that are robustly connected to the evolution of the Siberian High. Considering the regional average PM2.5 anomalies, our results show that polluted SWPs favourable for the occurrence of regional PM2.5 pollution account for 61-78 %. The most conducive SWP, associated with the highest regional exceedance (46 %) of PM2.5 levels, is characterized by noticeable cyclonic anomalies at 850 hPa and stagnant surface weather conditions. Our source apportionment analysis emphasizes the pivotal role of local emissions and intra-regional transport within the YRD in shaping PM2.5 pollution in representative cities. Local emissions have the most significant impact on PM2.5 levels in Shanghai (32-48 %), while PM2.5 pollution in Nanjing, Hangzhou, and Hefei is more influenced by intra-regional transport (33-61 %). Industrial and residential emissions are the dominant sources, contributing 32-41 % and 24-38 % to PM2.5, respectively. Under specific SWPs associated with a stronger influence of inter-regional transport from northern China, there is a synchronously remarkable enhancement in the contribution of residential emissions. Our study pinpoints the opportunities for future air quality planning that would benefit from quantitative source attribution linked to prevailing SWPs.
摘要：
了解在有利天气模式（SWPs）下发生的严重细颗粒物（PM2.5）污染事件的原因和来源对于区域空气质量管理至关重要。中国东部的长江三角洲（YRD）地区在2013年至2017年冬季反复出现严重的PM2.5事件。在这项研究中,我们采用了客观的分类方法，自组织地图，调查YRD中主要SWP对PM2.5污染的潜在影响。我们进一步使用集成在扩展综合空气质量模型（CAMx）中的颗粒源分配技术（PSAT）工具进行了一系列源分配模拟，以量化不同SWP下对PM2.5污染的源贡献。在这里，我们确定了YRD上的六个主要SWP，它们与西伯利亚高地的演变密切相关。考虑到区域平均PM2.5异常，我们的结果表明，有利于区域PM2.5污染发生的污染SWP占61-78％。最有利的SWP，与PM2.5水平的最高区域超标(46%)相关，其特征是在850hPa下明显的气旋异常和停滞的地面天气条件。我们的源分配分析强调了YRD内本地排放和区域内运输在塑造代表性城市PM2.5污染方面的关键作用。本地排放对上海PM2.5水平影响最大（32-48％），而南京的PM2.5污染,杭州,合肥受区域内交通影响更大（33-61%）。工业和住宅排放是主要来源，对PM2.5的贡献率分别为32-41%和24-38%。在特定的SWP下，与来自中国北方的区域间运输的更强影响相关，住宅排放的贡献同步显着增加。我们的研究指出了未来空气质量规划的机会，这些机会将受益于与现行SWP相关的定量来源归因。
