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Based on the observation data of ozone pollution in the Yangtze River Delta (YRD) region from 2017 to 2023, the self-organized mapping (SOM) typing method and the WRF-CMAQ model were combined to systematically investigate theanomaliesof typical weather patterns and meteorological factor that affected ozone pollution in the region. Six typical weather patterns were identified through SOM modeling: trough front retention (32.65%) was found to be the main pollution pattern in summer, pre-Meiyu rain (21.12%) was concentrated in the alternating period of spring and summer, subtropical high peripheral (18.95%) occurred mostly in fall, and there were also weak low-pressure (12.56%), high-pressure profrontal (9.13%), and anticyclone (5.6%) patterns. The main meteorological factors during the ozone pollution period were characterized by significant anomalies, specifically positive temperature anomalies, negative wind speed and humidity anomalies, and radiation enhancement, all of which contributed to the production and accumulation of ozone. Based on the WRF-CMAQ model simulations with process analysis method, the analysis of the typical pollution processes from 2017 to 2023 revealed that during the cold period from October to April, ozone formation was primarily driven by temperature and radiation anomalies, with photochemical reactions dominating. The contribution of photochemical processes to ozone formation was found to reach 53%. In contrast, during the warm period from May to September, ozone pollution was more influenced by dynamic processes, such as the reduction of wind speed in the boundary layer, leading to a decrease in the contribution of photochemical processes to 38%. Meanwhile, the contribution of horizontal transport significantly increased to 26%. This study clarified the physical and chemical mechanisms between meteorological factors and ozone pollution under different weather patterns, providing scientific support for the refined control of ozone pollution in the YRD region.

, authors=Wen-jing DENG^{1, 2}, Xin-yu XI¹, Jun-jie WANG¹, Yu-xuan HUANG¹, Yu-qi ZHU¹, Ao SHEN¹, Yi-xiao ZHU¹, Yi-ming LIU¹, Xiao LU¹, Qi FAN^{1, 2, *}, authorsList=Wen-jing DENG, Xin-yu XI, Jun-jie WANG, Yu-xuan HUANG, Yu-qi ZHU, Ao SHEN, Yi-xiao ZHU, Yi-ming LIU, Xiao LU, Qi FAN, authorCompany=null, correspAuthors=Qi FAN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314918250861920320, articleId=1314918249867870270, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=长三角臭氧污染天气分型及生成机制的过程分析, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

基于2017~2023年长三角地区臭氧污染观测数据，结合自组织映射（SOM）分型方法和WRF-CMAQ模式，系统探究影响该区域臭氧污染的典型天气型及气象因子异常特征．通过SOM分型，识别出6类典型天气型：槽前滞留型（32.65%）为夏季主要污染型，梅雨前期型（21.12%）集中于春夏交替时期，副高边缘型（18.95%）多发生在秋季，此外还包括弱低压型（12.56%）、高压前部型（9.13%）和反气旋型（5.6%）．臭氧污染期间主要的气象因子呈现显著异常特征，具体表现为温度正异常、风速和湿度负异常以及辐射增强等，这些条件共同促进了臭氧的生成和累积．基于WRF-CMAQ模式结合过程分析方法对2017~2023年典型污染过程进行研究发现，在10月~次年4月的冷期，臭氧生成主要由温度和辐射异常驱动的光化学反应主导，其对臭氧生成的贡献率达到53%；而在5~9月暖期，臭氧污染还受到边界层内风速降低等动力过程影响，导致气相化学过程的贡献降至38%，水平传输的贡献则显著增加到26%．本研究阐明了不同天气型下气象因子与臭氧污染之间的物理化学关联机制，为长三角地区臭氧污染的精细化防控提供了科学依据．

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邓雯静（2001-），女，重庆人，中山大学硕士研究生，主要从事大气环境数值模拟研究．发表论文2篇．dengwj56@mail2.sysu.edu.cn.

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邓雯静（2001-），女，重庆人，中山大学硕士研究生，主要从事大气环境数值模拟研究．发表论文2篇．dengwj56@mail2.sysu.edu.cn.

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