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基于空气质量模型CAMx的臭氧溯源技术(OSAT),对2015年7月京津冀13个城市O3污染及传输规律进行定量模拟,建立了京津冀13个城市间的O3相互影响矩阵,并分析了北京、天津、石家庄3个典型城市O3污染逐日输送特征.研究表明,京津冀13个城市O3污染受传输贡献显著(＞80％),而受本地源贡献相对较小,仅占6.9％(廊坊)～19.7％(北京),传输贡献中由京津冀区内城市间互相输送(区内传输)贡献范围为10.3％(沧州)～32.2％(廊坊),区外传输贡献约为37.3％(承德)～60.7％(秦皇岛),边界场BC贡献为14.4％(邯郸)～23.1％(张家口).典型城市O3逐日传输矩阵证明传输贡献占主导,尤以区外贡献最为突出,本地贡献相对较小,但在O3超标日,本地贡献明显上升.“,”By coupling ozone source apportionment technology (OSAT) with comprehensive air quality model with extensions (CAMx),the regional transport matrix of surface O3 was built and the spatio-temporal distributions were also analyzed in 13 cities of Jing-Jin-Ji Region in July,2015.Results showed that the major contributor to O3 was transport source (TS＞80％),while the local source (LS) contributed only 6.9％ in Langfang and 19.7％ in Beijing.The transport source included in-region sources (IRS,ranges from 10.3％ in Cangzhou to 32.2％ in Langfang),out-region sources (ORS,ranges from 37.3％ in Chengde to 60.7％ in Qinhuangdao),and boundary condition (BC,ranges from 14.4％ in Handan to 23.1％ in Zhangjiakou).The daily matrix of regional transport in key cities also showed the significance of ORS to O3.There was a positive correlation between LS contribution and the mass concentrations of O3-8h,in particular,the contribution of LS increased significantly during high ozone episode days.Regarding the regional characteristics of transport path to different cities,the ozone levels were influenced by both local and regional emission sources,and joint efforts are required to optimize the O3 reduction scheme.