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Analysis of PM2.5 Pollution Transport Characteristics and Potential Sources in Four Chinese Megacities During 2022: Seasonal Variations

文献类型：期刊论文

作者Mao, Kun2,3; Yao, Yuan2,3,4; Wang, Kun2,3; Liu, Chen1; Tang, Guangmin1; Feng, Shumin1,2; Shen, Yue1; Ju, Anhua1; Zhou, Hao1; Li, Zhiyu1
刊名ATMOSPHERE
出版日期2024-12-01
卷号15期号:12页码:28
关键词PM2.5 HASM MGWR potential source contribution
DOI10.3390/atmos15121482
产权排序3
英文摘要Atmospheric particulate pollution in China's megacities has heightened public concern over air quality, highlighting the need for precise identification of urban pollution characteristics and pollutant transport mechanisms to enable effective control and mitigation. In this study, a new method combing the High Accuracy Surface Modeling (HASM) and Multiscale Geographically Weighted Regression (MGWR) was proposed to derive seasonal high spatial resolution PM2.5 concentrations. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) was applied to analyze the seasonal spatial variations, transport pathways, and potential sources of PM2.5 concentrations across China's four megacities: Beijing, Shanghai, Xi'an, and Chengdu. The result indicates that: (1) the proposed method outperformed Kriging, inverse distance weighting (IDW), and HASM, with coefficient of determination values ranging from 0.91 to 0.94, and root mean square error values ranging from 1.98 to 2.43 mu g/m(3), respectively; (2) all cities show a similar seasonal pattern, with PM2.5 concentrations highest in winter, followed by spring, autumn, and summer; Beijing has higher concentrations in the south, Shanghai and Xi'an in the west, and Chengdu in central urban areas, decreasing toward the rural area; (3) potential source contribution function and concentration weighted trajectory analysis indicate that Beijing's main potential PM2.5 sources are in Hebei Province (during winter, spring, and autumn), Shanghai's are in the Yellow Sea and the East China Sea, Xi'an's are in Southern Shaanxi Province, and Chengdu's are in Northeastern and Southern Sichuan Province, with all cities experiencing higher impacts in winter; (4) there is a negative correlation between precipitation, air temperature, and seasonal PM2.5 levels, with anthropogenic emissions sources such as industry combustion, power plants, residential combustion, and transportation significantly impact on seasonal PM2.5 pollution.
WOS关键词LONG-RANGE TRANSPORT ; HAZE POLLUTION ; SHANGHAI ; AEROSOLS ; CITY ; INTERPOLATION ; PATHWAYS ; CHENGDU ; TRENDS ; IMPACT
资助项目Natural Science Foundation of Sichuan Province in China ; Open Fund of Sichuan Provincial Key Laboratory of Artificial Intelligence[2021RYJ03] ; [2023NSFSC0752]
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001385505400001
出版者MDPI
资助机构Natural Science Foundation of Sichuan Province in China ; Open Fund of Sichuan Provincial Key Laboratory of Artificial Intelligence
源URL[http://ir.igsnrr.ac.cn/handle/311030/211983]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Yao, Yuan
作者单位1.Chengdu Univ technol, Coll earth Sci, Chengdu 610059, Peoples R China
2.Chengdu Univ, Sch Architecture & Civil Engn, Chengdu 610106, Peoples R China
3.Chengdu Univ, Key Lab Pattern Recognit & Intelligent Informat Pr, Chengdu 610106, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Mao, Kun,Yao, Yuan,Wang, Kun,et al. Analysis of PM2.5 Pollution Transport Characteristics and Potential Sources in Four Chinese Megacities During 2022: Seasonal Variations[J]. ATMOSPHERE,2024,15(12):28.
APA Mao, Kun.,Yao, Yuan.,Wang, Kun.,Liu, Chen.,Tang, Guangmin.,...&Li, Zhiyu.(2024).Analysis of PM2.5 Pollution Transport Characteristics and Potential Sources in Four Chinese Megacities During 2022: Seasonal Variations.ATMOSPHERE,15(12),28.
MLA Mao, Kun,et al."Analysis of PM2.5 Pollution Transport Characteristics and Potential Sources in Four Chinese Megacities During 2022: Seasonal Variations".ATMOSPHERE 15.12(2024):28.

入库方式： OAI收割

来源：地理科学与资源研究所

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