Understanding meteorological influences on PM2.5 concentrations across China: a temporal and spatial perspective

文献类型： 外文期刊

第一作者： Chen, Ziyue

作者： Chen, Ziyue;Xie, Xiaoming;Chen, Danlu;He, Bin;Chen, Ziyue;He, Bin;Cai, Jun;Xu, Bing;Gao, Bingbo;Cheng, Nianliang

作者机构：

期刊名称：ATMOSPHERIC CHEMISTRY AND PHYSICS （ 影响因子：6.133； 五年影响因子：6.546 ）

ISSN： 1680-7316

年卷期： 2018 年 18 卷 8 期

页码：

收录情况： SCI

摘要： With frequent air pollution episodes in China, growing research emphasis has been put on quantifying meteorological influences on PM2.5 concentrations. However, these studies mainly focus on isolated cities, whilst meteorological influences on PM2.5 concentrations at the national scale have not yet been examined comprehensively. This research employs the CCM (convergent cross-mapping) method to understand the influence of individual meteorological factors on local PM2.5 concentrations in 188 monitoring cities across China. Results indicate that meteorological influences on PM2.5 concentrations have notable seasonal and regional variations. For the heavily polluted North China region, when PM2.5 concentrations are high, meteorological influences on PM2.5 concentrations are strong. The dominant meteorological influence for PM2.5 concentrations varies across locations and demonstrates regional similarities. For the most polluted winter, the dominant meteorological driver for local PM2.5 concentrations is mainly the wind within the North China region, whilst precipitation is the dominant meteorological influence for most coastal regions. At the national scale, the influence of temperature, humidity and wind on PM2.5 concentrations is much larger than that of other meteorological factors. Amongst eight factors, temperature exerts the strongest and most stable influence on national PM2.5 concentrations in all seasons. Due to notable temporal and spatial differences in meteorological influences on local PM2.5 concentrations, this research suggests pertinent environmental projects for air quality improvement should be designed accordingly for specific regions.

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